Multi-class Fruit Classification using RGB-D Data for Indoor Robots

Abstract

In this paper we present an effective and robust system to classify
fruits under varying pose and lighting conditions tailored for an
object recognition system on a mobile platform. Therefore, we present
results on the effectiveness of our underlying segmentation method
using RGB as well as depth cues for the specific technical setup
of our robot. A combination of RGB low-level visual feature descriptors
and 3D geometric properties is used to retrieve complementary object
information for the classification task. The unified approach is
validated using two multi-class RGB-D fruit categorization datasets.
Experimental results compare different feature sets and classification
methods and highlight the effectiveness of the proposed features
using a Random Forest classifier.

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BibTeX

@inproceedings{jiang_ROBIO2013,
author = {Jiang, Lixing and Koch, Artur and Scherer, Sebastian A. and Zell,
Andreas},
title = {Multi-class Fruit Classification using {RGB-D} Data for Indoor Robots},
booktitle = {IEEE International Conference on Robotics and Biomimetics (ROBIO)},
year = {2013},
address = {Shenzhen, China},
month = {December},
abstract = {In this paper we present an effective and robust system to classify
fruits under varying pose and lighting conditions tailored for an
object recognition system on a mobile platform. Therefore, we present
results on the effectiveness of our underlying segmentation method
using RGB as well as depth cues for the specific technical setup
of our robot. A combination of RGB low-level visual feature descriptors
and 3D geometric properties is used to retrieve complementary object
information for the classification task. The unified approach is
validated using two multi-class RGB-D fruit categorization datasets.
Experimental results compare different feature sets and classification
methods and highlight the effectiveness of the proposed features
using a Random Forest classifier.},
url = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2013/jiang_robio2013.pdf}
}